fix(rebase): remove duplicate definitions and update stale module paths

Rebase left duplicate function blocks in worker.py (triple human_message
write causing 3x user messages in /history), deps.py, and prompt.py.
Also update checkpointer imports from the old deerflow.agents.checkpointer
path to deerflow.runtime.checkpointer, and clean up orphaned feedback
props in the frontend message components.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -164,30 +164,6 @@ Skip simple one-off tasks.
 """
 
 
-def _skill_mutability_label(category: str) -> str:
-    return "[custom, editable]" if category == "custom" else "[built-in]"
-
-
-def clear_skills_system_prompt_cache() -> None:
-    _get_cached_skills_prompt_section.cache_clear()
-
-
-def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
-    if not skill_evolution_enabled:
-        return ""
-    return """
-## Skill Self-Evolution
-After completing a task, consider creating or updating a skill when:
-- The task required 5+ tool calls to resolve
-- You overcame non-obvious errors or pitfalls
-- The user corrected your approach and the corrected version worked
-- You discovered a non-trivial, recurring workflow
-If you used a skill and encountered issues not covered by it, patch it immediately.
-Prefer patch over edit. Before creating a new skill, confirm with the user first.
-Skip simple one-off tasks.
-"""
-
-
 def _build_subagent_section(max_concurrent: int) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 

backend/packages/harness/deerflow/client.py

@@ -374,7 +374,7 @@ class DeerFlowClient:
         """
         checkpointer = self._checkpointer
         if checkpointer is None:
-            from deerflow.agents.checkpointer.provider import get_checkpointer
+            from deerflow.runtime.checkpointer.provider import get_checkpointer
 
             checkpointer = get_checkpointer()
 
@@ -429,7 +429,7 @@ class DeerFlowClient:
         """
         checkpointer = self._checkpointer
         if checkpointer is None:
-            from deerflow.agents.checkpointer.provider import get_checkpointer
+            from deerflow.runtime.checkpointer.provider import get_checkpointer
 
             checkpointer = get_checkpointer()
 

backend/packages/harness/deerflow/runtime/runs/worker.py

@@ -85,64 +85,6 @@ async def run_agent(
     pre_run_snapshot: dict[str, Any] | None = None
     snapshot_capture_failed = False
 
-    # Initialize RunJournal for event capture
-    journal = None
-    if event_store is not None:
-        from deerflow.runtime.journal import RunJournal
-
-        journal = RunJournal(
-            run_id=run_id,
-            thread_id=thread_id,
-            event_store=event_store,
-            track_token_usage=getattr(run_events_config, "track_token_usage", True),
-        )
-
-        # Write human_message event (model_dump format, aligned with checkpoint)
-        human_msg = _extract_human_message(graph_input)
-        if human_msg is not None:
-            msg_metadata = {}
-            if follow_up_to_run_id:
-                msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id
-            await event_store.put(
-                thread_id=thread_id,
-                run_id=run_id,
-                event_type="human_message",
-                category="message",
-                content=human_msg.model_dump(),
-                metadata=msg_metadata or None,
-            )
-            content = human_msg.content
-            journal.set_first_human_message(content if isinstance(content, str) else str(content))
-
-    # Initialize RunJournal for event capture
-    journal = None
-    if event_store is not None:
-        from deerflow.runtime.journal import RunJournal
-
-        journal = RunJournal(
-            run_id=run_id,
-            thread_id=thread_id,
-            event_store=event_store,
-            track_token_usage=getattr(run_events_config, "track_token_usage", True),
-        )
-
-        # Write human_message event (model_dump format, aligned with checkpoint)
-        human_msg = _extract_human_message(graph_input)
-        if human_msg is not None:
-            msg_metadata = {}
-            if follow_up_to_run_id:
-                msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id
-            await event_store.put(
-                thread_id=thread_id,
-                run_id=run_id,
-                event_type="human_message",
-                category="message",
-                content=human_msg.model_dump(),
-                metadata=msg_metadata or None,
-            )
-            content = human_msg.content
-            journal.set_first_human_message(content if isinstance(content, str) else str(content))
-
     journal = None
 
     # Track whether "events" was requested but skipped